IM Alg1.1.14 Lesson: Outliers

The histogram and box plot show the average amount of money, in thousands of dollars, spent on each person in the country (per capita spending) for health care in 34 countries.
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2BkozoozKPw2VkCEpiBgMI8EdS7d+82Fy5caDjHgVgPLQrzUGLj2ivM4/jZWwISmJ6AwjwRU8T40qVLo60pzKMRDjKgMA/CZWMJSKACAYV5Isg3b95srl69Otqawjwa4SADCvMgXDaWgAQqEFCYJ4KMMJ89e7Y5Ojo6Pi5evHj8kfYm87/7u7/blMc3/q03NK/+jr/X/Oe//L+dON7+3Gead336i7MeRx94vnnLs5+b1UfbGB6/90KDb0SydlGYaxPXnwQksI2AwryN0I7XEeozZ840Dx8+7LRw//795tatWyeOb3jNa5szb/y7zX/07p8/cSjMnRhHXVCYR+GzswQkMAMBhXkGqGGS75wR6CHFj7KH0BrfVmEez1ALEpDAtAQU5ml5nrCGMPNEPKQozENojW+rMI9nqAUJSGBaAgrzRDz5qexcbt++vfWj7Nw+XivMQaLOWWGuw1kvEpBAfwIKc39WG1vyy0T4wS9+uQg/BMb/afb/Mff7YTV/+Ksfp7YfnNu1jh/y4wfuLBKQwPoIKMwT5+TRo0ejLPrEPArf4M4+MQ9GZgcJSGBmAgrzzICHmleYhxIb115hHsfP3hKQwPQEFObpmY6yqDCPwje4s8I8GJkdJCCBmQkozDMDHmpeYR5KbFx7hXkcP3tLQALTE1CYp2c6yqLCPArf4M4K82BkdpCABGYmoDDPDHioeYV5KLFx7RXmcfzsLQEJTE9AYZ6e6SiLCvMofIM7K8yDkdlBAhKYmYDCPDPgoeYV5qHExrVXmMfxs7cEJDA9AYV5eqajLCrMo/AN7qwwD0ZmBwlIYGYCCvPMgIeaV5iHEhvXXmEex8/eEpDA9AQU5umZjrKoMI/CN7izwjwYmR0kIIGZCSjMMwMeal5hHkpsXHuFeRw/e0tAAtMTUJinZzrKosI8Ct/gzgrzYGR2kIAEZiagMM8MeKh5hXkosXHtFeZx/OwtAQlMT0Bhnp7pKIsK8yh8gzsrzIOR2UECEpiZgMI8E+ArV640t27dGmxdYR6MbFQHhXkUPjtLQAIzEFCYZ4B6/fr15ty5cw3noUVhHkpsXHuFeRw/e0tAAtMTUJgnZnr37t3mwoULx6KsMH+xedentx+P33uhOfrA8w0iWbsozLWJ608CEthGQGHeRmjA9UePHjXnz59vHjx4oDD3EOQQbYV5+81LsJrq/JZnP3d8MzRgettUAhKoREBhnhA03yvfvHnz2CJPy9uemP/gD/6gKY+/9YZval7z5u9ufuQjnzxxvP25z/R6+hyzcfPUyoY9xsYufRVmhXnCZagpCew9AYV5ohTevn27uXTp0kvW+gjz/fv3m/e///0njld/wzc0r/o7/2bz7//EPzpxKMwvoZ30hR9lT4pTYxKQwAQEFOYJID58+PD4h704R+kjzNE2n/3hr0xj/tcK8/yM9SABCQwjoDAP49Xamh/4Ojo66jxaO3VUKswdYGaqVphnAqtZCUhgZwIK887oNnf0ibn/96Z+x9yf1S7f4bf18Ye/Nq9fr0pgSQIK80z0Feb+YqMw92fVJrK71CnMMy18zUpgAgIK8wQQ20zwfXP+zrmtTVudH2W3UZmvzo+y52OrZQlIYDcCCvNu3GbrpTDPhrbVsMLcisVKCUhgQQIK84Lw21wrzG1U5qtTmOdjq2UJSGA3Agrzbtxm66Uwz4a21bDC3IrFSglIYEECCvOC8NtcK8xtVOarU5jnY6tlCUhgNwIK827cZuulMM+GttWwwtyKxUoJSGBBAgrzgvDbXCvMbVTmq1OY52OrZQlIYDcCCvNu3GbrpTDPhrbVsMLcisVKCUhgQQIK84Lw21wrzG1U5qtTmOdjq2UJSGA3Agrzbtxm66Uwz4a21bDC3IrFSglIYEECCvOC8NtcK8xtVOarU5jnY6tlCUhgNwIK827cZuulMM+GttWwwtyKxUoJSGBBAgrzgvDbXCvMbVTmq1OY52OrZQlIYDcCCvNu3GbrpTDPhrbVsMLcisVKCUhgQQIK84Lw21wrzG1U5qtTmOdjq2UJSGA3Agrzbtxe0evRo0fN008/3Vy+fPn44DV1Q4vCPJTYuPYK8zh+9paABKYnoDBPxPTSpUvNtWvXmrt37x4fV65caS5evDjYusI8GNmoDgrzKHx2loAEZiCgMM8AFZM8LR8dHTUPHz4c5EFhHoRrdGOFeTRCDUhAAhMTUJgnBhrmeHI+c+ZMvO19Vph7o5qkocI8CUaNSEACExJQmCeEydPxvXv3mhs3bjTnzp1rbt26tdH65z//+aY83vDN55rXvuU/aP6rf/zgxPH25z7TvOvTX5z1OPrA881bnv3crD7axvD4vRcafD/xT/6v5sb//k+rHvjENzG0xTZnHazxPaePLtvhe+ME9aIEJLAIAYV5QuwIMd81c1y4cOH4h8E2mb9//37z3ve+98TxdV//6uZr//a/0fy7/+W7ThynQZgRqaUOhXnTTPWaBCRQk4DCPBPtBw8eHH+UzXlIOc0fZS8hjvG0voTveGrteqqdsz58D5mbtpWABOoQUJhn5MyT8/Xr1wd5UJjn/bi+FDuFedD0tLEEJFCBgMI8EeTy/yzz/uzZs41PzNuFdklxXNJ3PLWWNws13ofviaa/ZiQggQkJKMwTweQnsPnlIvzg12OPPXYsyvy/5qHFJ+btQj6lcCnMQ2eo7SUggbkJKMwTEeYJmf8ixQ+AcR76/5cjDIVZYZ7yxqPLlk/MseI8S2B9BBTmleVEYVaYu8R0ynqFeWUL33AkkAgozAnGGl4qzArzlALcZUthXsNqNwYJtBNQmNu5LFarMCvMXWI6Zb3CvNgS17EEthJQmLciqttAYVaYpxTgLlsKc911rTcJDCGgMA+hVaGtwqwwd4nplPUKc4XFrAsJ7EhAYd4R3FzdFGaFeUoB7rKlMM+1grUrgfEEFObxDCe1oDArzF1iOmW9wjzpstWYBCYloDBPinO8MYVZYZ5SgLtsKczj16oWJDAXAYV5LrI72lWYFeYuMZ2yXmHecYHaTQIVCCjMFSAPcaEwK8xTCnCXLYV5yKq0rQTqElCY6/Le6k1hVpi7xHTKeoV561K0gQQWI6AwL4a+3bHCrDBPKcBdthTm9vVnrQTWQEBhXkMWUgwKs8LcJaZT1ivMadH5UgIrI6AwrywhCrPCPKUAd9lSmFe28A1HAomAwpxgjH354MGD47/HzN9kvn379k7mFGaFuUtMp6xXmHdannaSQBUCCvNEmG/evNmcO3euuX79+vHB62vXrg22rjArzFMKcJcthXnw0rSDBKoRUJgnQv3w4cMTlnh/dDQcr8KsMHeJ6ZT1CvOJ5eobCayKwHDlWFX46w3m0aNHCvOn+4ns4/deaI4+8HzDeUrx6WNrSd8hjn3inLpN+F7vCjIyCZxeAgrzTLm/detWc+nSpY3W//zP/7wpj28+f7557Xddaf6bu3/4imPqzbm0hziyYZf1c79fUhyX9B3iODffNvvhe+ME9aIEJLAIAYV5Buz8EBjfMXPeVO7fv9889dRTJ44zX/d1zb/0LX+n+Xd+8L8+cbz9uc/MLpgKc78n/Dah26UuxHGXvmP7hO9N89NrEpDAMgQU5om5I8Znz55teGLepfgdc11x9Il5l1lqHwlIYE4CCvOEdMeKMqEozArz2KfhPv19Yp5w4WtKAhMTUJgnAjqFKBOKwqww9xHWsW0U5okWvmYkMAMBhXkiqGfOnGkuXrz40i8Y4ZeMcNy9e3eQB4VZYR4run36K8yDlqWNJVCVgMI8Ee74xSLlWWHeLrRLfs+7pO8Qxz5COnWb8D3R9NeMBCQwIQGFeUKYU5jyiXm7kE8pUgrzFLNWGxKQwJQEFOYpaU5gS2FWmKe88eiy5RPzBItVExKYiYDCPBPYXc0qzApzl5hOWa8w77pC7SeB+QkozPMzHuRBYVaYpxTgLlsK86BlaWMJVCWgMFfFvd2Zwqwwd4nplPUK8/a1aAsJLEVAYV6KfIdfhVlhnlKAu2wpzB0L0GoJrICAwryCJOQQFGaFuUtMp6xXmPOq87UE1kVAYV5XPvzNXz3/VORUIuV/l1rZAjAcCUigUZhXNgl8YvaJeaqbjk12fGJe2cI3HAkkAgpzgrGGlwqzwrxJUKe6pjCvYbUbgwTaCSjM7VwWq1WYFeapxHeTHYV5sSWuYwlsJaAwb0VUt4HCrDBvEtSprinMdde13iQwhIDCPIRWhbYKs8I8lfhusqMwV1jMupDAjgQU5h3BzdVNYVaYNwnqVNcU5rlWsHYlMJ6Awjye4QkLjx49ap5++unm4cOHJ+r7vlGYFeapxHeTHYW574q0nQTqE1CYJ2T+4MGD5uLFi82ZM2eaoX+HOcJQmBXmTYI61TWFOVacZwmsj4DCPFFOeEI+d+5cc/v2bYV54C8JWfKXfCzpO8RxKrEdYid8TzT9NSMBCUxIQGGeEGZ8fH10dOQT8wBxXlIcl/Qd4jhEUKdqG74nnP6akoAEJiKgME8EMpvpK8x/9Vd/1ZTHuX/lW5rX/3v/oPmHv/vPXnFMtSl32Tn6wPMNG3bX9bnqlxTHJX2HOM7FdZPd8H3vn/3zZonj0V9+OS+ZU/H64V/85SKsye//8//+1algfCiDVJhnyGRfYb5//35z7dq1E8erzpxp/sVv/tvNd/wn/8WJ4+3PfWZ2wVSY636/HeK4SUDnuha+yfkSx5Xf+NwMK2/dJl/1i//HIqzJ72Mf/6N1wzG6EwQU5hM4pnnTV5jbvPnDX3XF8bQ/MTP+2sfrnvl0c+n2Z9um/0HXIZAX/pfPVOf99//xHyvMezazFOYZEqYwDxPXJcVxSd/x1DrXU/Emu0v6Pvcrv39qhRnum/IyxzXmuE/MM2z0M5pUmGeAqzArzH022CXFcUnfCvOw9dFnLm1qozDPsMnPbFJhngGwwjxs41nyqXVJ30uK45K+FeZh62OT6Pa5pjDPsMnPbFJhngHw9evX/c1f/neprR9ZLimOS/pWmBXmGbbdgzKpMK8snf7wV/1Nix/K4amiz9PHlG2WFMclfSvM9ee43zGvbKPfEo7CvAVQ7csKc/1NS2Guy1xhrsvbj7Jr7+Lj/SnM4xlOakFhrr9pKcx1mSvMdXkrzJNu0VWMKcxVMPd3ojDX37QU5rrMFea6vBXm/vvvWloqzGvJxItxKMz1Ny2FuS5zhbkub4V5ZZt8j3AU5h6QajZRmOtvWgpzXeYKc13eCnPNHXwaXwrzNBwns6Iw19+0FOa6zBXmurwV5sm252qGFOZqqPs5Upjrb1oKc13mCnNd3gpzv713Ta0U5jVlo2kahbn+pqUw12WuMNflrTCvbJPvEY7C3ANSzSYKc/1NS2Guy1xhrstbYa65g0/jS2GehuNkVhTm+puWwlyXucJcl7fCPNn2XM2QwlwNdT9HCnP9TUthrstcYa7LW2Hut/euqZXCvKZs+B1z9d9XzaalMNcVCoW5Lm+FeWWbfI9wFOYekGo28Ym5/qalMNdlrjDX5a0w19zBp/GlME/D8djKo0ePmjt37jQ3btxo7t69u5Nlhbn+pqUw12WuMNflrTDvtBUv2klhngg/onzx4sXm0qVLDX+P+dy5c8fnoeYV5vqblsJcl7nCXJe3wjx0F16+vcI8UQ4QY0Q5ysOHD5ujo6MGwR5SFOb6m5bCXJe5wlyXt8I8ZAdeR1uFeaI8IMq3bt06Ya2t7kSDljcKc/1NS2Guy1xhrstbYW7ZaFdepTBPlKAzZ8684nvlK1euDP44+w3ffK4583f/XvMf/uJvVD+O3vFLzb/2332kut9/++eebfDNufa4l/QNa8Zde8z4W9L3q9/9PzX/+j/6n5tf/Pgnqh//48fq+4xxLrm+Lr//zkQ7nWZqEFCYJ6LMx9blD3zx8TZHV/m5n/u55tu//dtPHF//6m84/ggcex4ycA44B6aYA9/x/f9Z1zZk/QoJKMwTJeXChQuvEOarV69uFOa//uu/bsrjj/7oj5qPfexjrcdv/uZvNhQyr6UAACAASURBVB4ycA44B3aZAxNtdZqpQEBhnghy2/fJ1N28eXMiD5qRgAQkIIHTQEBhnijLCDBPyFH4aWw+gnrw4EFUeZaABCQgAQlsJaAwb0XUrwFCzA+APfnkk83TTz99/H+as1D3s2IrCUhAAhI47QQU5glnAOLMD3tdu3btFf91akI3mpKABCQggQMmoDAfcHIdmgQkIAEJ7B8BhXn/cmbEEpCABCRwwAQU5gNOrkOTgAQkIIH9I6Aw71/OjFgCEpCABA6YgMJ8wMl1aBKQgAQksH8EFOb9y5kRS0ACEpDAARNQmFeUXP67Ff8Pmv9uVbPwO76feOKJ5vLly81jjz32il8tOlcsjJf/843f2r7LMfErVflNbTUKv3Qmxhzn2jnnF+LU9N025vBf45fwMNdijuP3mWeeqZHqYx/ZN+tr7vHGPlL+tTuCybHAgz9Pa1kfAYV5JTlhsV68eLHhL1LVEgiGjl9E6fbt28eCzGLmN5bVWLDx29K4MeDgPb+kpYbvnHZE8dy5c9W4M9b43eox9rk36zzeuAnBN35r+EYQYqxxZq6Rb67NXRgzv/CHseKf95v+wMxU8TC28+fPH/vCL2Nmrs01xxkf+0jb+MpYYr1NNVbtTEdAYZ6O5ShLbBIsWhZvTWFuCxr/NTatLt9wqFXyJl2LOzdBtXyVHEMYyvol3nNDVOu343GzmQuiVCMH8EYkc2FtzTVuxsT8wke5httioX3N9ZY5+LqbwMnZ2t3OK5UIrEWY2bhqF+7oz549W+UJjrHFEwRPGWxiNTZq/OKr9kfXkcu1bMSR67meHGO8ceYpNQvQLn8rPWwNOZPrUoRrrHH8cuRCHGVdW3y5j6+XIaAwL8O902uNRdvp/MWPtmt9vEgcbMw3btw4Pto+ftsU69hrbM5xA8IGVUuY8Zn/xi7feZL3GoXc4h+f3ATV9J3HB2/41yrcfCHO8T1zrRsjbgb4aDmXyH+um/o1fDlyYX6XdbyvNe9zLL7eTEBh3syn+tUlhZmnGDaR/GQxNwCEmc2BTRph5offiGPuUn6cvOQGxUZd67t1bgjgHE+qzLeaN2KRV3zWuhnBJ8LMd708NSLQ/BBWjXmGb3jjjxtQbgx4P7cYMp85cmkTZtb63LHkGHzdj4DC3I9TtVZLCfMSotwGtcZHjIgST4t37txp7t27d3ywYXJTwvslCptjjRsihLkURHxzo1Kr1BaDyDfiHAWB5tOCWiW+94U9N2Jzi2GbMHd9lD13LLUYH5IfhXll2VxCmNciyqSCDWXujzjZoNmM8sFTFE9xS21S+K0hjjytLS3M5fe9cy/B8tMR/DHnyx8ImzuOsI9AIs5zljZhxie+c+F9rY/1s19fbyagMG/mU/1qbWFeUpTxXRaEY+5Nq/TJezayWqJcCiPCUevjZMbJx6pR4mkyPtqO+rnOPC0jzDULvPkYO8836mBeu9TKdZswk+M8z+ABl3I+1maiv1cSUJhfyWTRGhZJLYFgoCzg+CEgPtrLx9wgeDLm4+PwySYx99Ny15hqCjNPKDytMW7YczOSP2btinGKejbjzB3/NT5Cj9iXuvGCOTcEfGXBjQkCVeMTCsaN7/ihs1q5bhNmYuGmFw7MvfjOPXLjeT0EFOb15GKRSLiL5mag7agREIIUvms9tdUYVx8fS443uC8ZQx9GU7bJcz0/PU/po83WEqwZa1dug0Otm8E2JtZtJqAwb+bjVQlIQAISkEBVAgpzVdw6k4AEJCABCWwmoDBv5uNVCUhAAhKQQFUCCnNV3DqTgAQkIAEJbCagMG/m41UJSEACEpBAVQIKc1XcOpOABCQgAQlsJqAwb+bjVQlIQAISkEBVAgpzVdw6k4AEJCABCWwmoDBv5uNVCUhAAhKQQFUCCnNV3DqTgAQkIAEJbCagMG/m41UJSEACEpBAVQIK84u4+b2xc/3+XGzzS+Nrll/4hV84/uMI23wuEdu2mOa6zh8Hyb8fmN/R3fX7hOeIofQ/hw9tbibAH5So+Uc7NkdT/2rt8fPHNPhDNRxD/mrcrvvSl770peN9L/+BEv5wyFNPPVUf9giPCvOL8PhrP0yiXPImnuuHvkYAav/tV4SZvxyUCyJU3nwsEVuOqeZrcsB4o7TlPK7NcS79z+FjbTaZbzVufsgrfzEq+2pbv9wclet8bczmjKfm+OEcf8WL/LTlo2usu+5LCDPrLAvzt33bt1UTZsaI/yFjbWOgML9IhURm0WJiMImnKLtOsjG+24SZ8RBLLkvElv3XfF0KI2PPG/ncsZT+5/a3BvtszjWEEAHIftgY29ZvTWFaA/8yhprjxxdP6LuUXfelpYWZsV69evX4E4Jdxh19BgkzsPrcCdAui1w4y2eu37t3r5c9Nk/atm2iQ+z0jZ84gdu2sGMMcOiKKdrEuZxkfeKIcYWNtvMmLqUw07ZNGHaJLccSccKC110leHVdj3riactzXI8z7fC5qQSfiKtt/F39o++mWKJNnzWBn+yfPn37dcXYVh8xdcU9h9+uXMCdjzCzYLbFTEzbctnWL+r4aBphjjxTj8+29ZuFKVjlfmEzn6dkFmPl3FW6eJbtw9am+LmW96k8/mwvfHbNm9w2Xge/eF+e+Qh5W+5znxgPdcTDemkrMaa2cQ8R5oh/25jDX5mzLmbYy2u9bQzb6tpHnnrhgInPH9XmNQcfkeaPCqI5d0fRhjN/kDzDi8VCX2zQZlPiGHj2S/v8PQWTjDoWJWds0idKJBeg4a+tHe2pj1j4Q+K8z0fYzTGF323fH0ccedxdccALe1wP+zdu3IghHZ9JfHCJNmyAmXUWZvKXx8Lr2LQiNtpkRtgvJ+KJIF78A/A5Tl7nO2R8YDfGEzE8/fTTpanjvMZ1zownLxjiJL4yzjLnGCZu+oe96Md77EQpN6l4z01Z9OVM/Jlt5Ih5Qp+YL+Qijz/8xBlbzN/IHe+JLdYSZ+ryuKMvPoirqwQf+scRdulTzj3alHmI9Vn6oJ72UWLOlD6DM+3KNRcxhQ3eB8e4BhtsZNa05z31ee2HHc7kILO5cuXKSwzCduSdtuSonJPsVWVpm2tDmOErCjwi77FmGVPOdTnvMs+ww3iIK2zF+Eo2MGNMcZ3zk08+ecwq9jlslmOkHfw2FViW/rEdpc1mZhHt4lzOTWwzHmLJBVZtecvzpY8wYyf2h8gFPnMuGCMx53kcXPL4on9ci3i5KSnr4lqf88mRt/QADs5jYtMkJlAeCMlm82AgFGCVmwltAEB9ttfi9hgSExNfAR5/8Zo+JDTHAAjsR8EH8ee4uIZNbGdbtMsTlqS0TSb85Q2P8dK3XBgRA+e2OPBdxkvb8BuxYR/+TIZceB9tOJesszBHP+IsuUdsTKRg2RVb2OEc/SLf1NE/bPCeseAzx87rMg545jkW/lmEUcJfnzhhQbvgQ0y8L/0SX5lz2mRxDf65Hdez/eCf20Tc+YztMobYgIIbsWf/9I85lllnu/TFbtdaif55jgbznBvib5vz1GM/SuSia11FO84l47iGPdYqtiNPnKnPMdE+5ky0CxtxbuvTNZaYk5lFjCf7bauLOPL67/JT1rPWy40aH7lgP+YB9cwD9qlcGGteK1zDF/W5L3OBdjFnYEcd7WhPCd6ZBTaiT/Ybr+kT+3LU0R5fYTfqu3If1znnuRv1cMEesebCmivXHe/zPtFHmLEJ65hPnJnLOT/EwDipL/NEXNuYwSJrUR5Hn9cnR97SgyDKjYJm5QYCyDxhaRMTOczGBCoHGtfzGZ/4GFKwm5MZ74kjFxJBvBku/fLEYlJx9CnbJmBXHFEfEyQ20LzA8A+LbbGUbYYKc5m7iK1r/HF90yImZhZOWajPi6CcS7QPFvihhL+uOINhtCsZxlwMe9gs88b7tjnHhsa1KGU/6kv+0TafmWPYKgs+o75tQW9bC/Rtizv8wDrHH/X4yhs/77va9VlXkbM8J9pY4b+LBWPJGy1tiT/4ROxxjnznvHKtayzE0zUn4Rylyydx5A23y09Zj9+2p/Lw13aOseW5DLccZ/SjPq8N3pf7XoggsVHife4X9rrOXXMx1lesQ/p35T7bJpa2uQtnxhCFGHmfWXAtxhBzrq8wh904l/kK9iVD2pesw0Y+R7y5bsjrl0fe0astwTQFOgclBvHEE080fOwaB+/pH9AYPO/7FGznzburD775eAmf4S/aRlycy4L9PMGJKyYsbfP4yr5Mhjt37rw0zrjzL9vF+4gjOJT1ER83CsQR/OIcH99EP84sAL47ym0iH1wfKsx5QdE/Ys4+y9f4Y2PnY6xywdCW65lp9KcuCwJjZtOKscSZ+lgYEc82hl1zjH7YC9Zt8XXlHJuZbdum3VYX440z/tt40Dfsx0aTN8vyJjLsxbmLc1xHiNr8BtPIfTnO6E89sUeJfpllXNvGOLfLY4z6sB3zqY1HtOVcto9rXWOBVdu+Qn3kABswb2MWArTNT+mfOLHJWn7mmWei+yvOtCv3M+qiwDfWRNRxznMrmOR+0Ta3oy4EkL2zrX30i3MXvz7rK2zkc8k9rpVzjvdtAk77PKa+wrxt/wyGEU8+oxv43MQs+vdhmm3H65dXW9QUZwJoW0AEFxM5gqAOgOURi6yEXbg68RbbtO8qTAQEMTYd2sYkiz4RVxuccoLl5NK/a8IwRhYYvmKcTJhNsUYcEVecoz7iwwaCFXbLc/SLzYENJtqU8Q4V5rAd54gt3nediQXf8GPTiU2e9tQTX1moo30UXmeeMSbOwaYrnqiPdvTJoh8+OOMn2vG+jK9kGH2xybUo4ZOFyU0EZ+ZEedMQ7eOM//wpTdSX9slriEfceWeu0S/O2+YffvFRlhhHxF3GEe2px0aU6JdZxrVtjLvaRT1nxsM6o8Cra0PmesRS8ukaS5nzYyct670rV+Ev+nX5aatnH6SeucIczd9Xx34WuaRd7GeZc8k34qCePpSIsWTCtbbx0z58sYZjPoTtfKZ/5CbX87qMrc1X2afLXowh2rfxjGuhAbzvI8zsWfDftH+W/sNXnDMzdKhkFv0571JeXm0dvXPCcxOAxuZBUGVSctt4DVza9SnY5+gqTODwH21iE4v3m+DkZNK+HGeb/7BfJoG2sSjCdz5HHLmO11EfyQvBLdvl98G63OBZWJlXLWGO2IgLpjknxENcZYEVbaPAvhxPXItzsIr3cY76YIidtjlWtqN/mTfeZ4bho21TYJwcXMNn2yYY/eNczrGoL/3GPMMm/DLT6JPP9G/jHG3KuR714SfeMxZ+KKYs1GembSyjD+0iF9QRG/3LUrbL1+EZHxcTe5cQ0KcrFny25bIrHupze8SzLe5yfnX5IeZsL4+P12EnWNG2zHPb2Lq4UR/xxh4RtrPv3C7X85obB+LourGlDdfbxhWx5r2RdhFT6SveM+Y2e/Qj1ijl+6jnTDt4UrYJc3wCE+3DDvZzHDGeuN51Zo0yhpJZ9I+H0q7+XfUvj7yjBYPmLiqXtsEhlJs2B/pvgpvt8xpwDLZrw8vJiL74z8kMOGVcMXERwij0I74obQurLX7iI87cN2zEOeKI93GOes6U4JrjirZxjj4lF/jniVVbmImPnLGhRSGe2GCjjnM5V5jY5RzL7Xkd4+6qD4aR23gf7eOmJ9cTX84b7zPD6EubXI+NciFG203ntrUU86fcKGBEzPBEQDcV4tu0VpjLbXlgXeRxEQMxlnOLNm3rKrOM+GiX6+mbGXe1i3rO+MdO3Dhs2txizZSM8Nl2k9EVD/UcUWDTxow2WUCDffSLc7keoz6faRN5b7sRwHbJs3wf9qjPnNvsxdrI7aJ/nMkdtso5ENeJt22uYZPx5NLFOrfpsgdj4ogSsee5xbVyjmwT5hgf9nIp8xXtcpuu19E2z9NYS119ttW/PPKOlsAhyXyPyHeafLfKhKcuJy8A8bEebWjLRzV5EvA6w+5weVyNbe6W8cV3MmEvFmBco56D+Igp2w9g1PORI+2IjYlF/1zol2ONjTx8E09MjmyL+LCV+2a7vI44uuq5HiUYBW9ixl+INclnPHCOsSNqxJA3ljZhZvLRNvr1iS3iKs+wyDHGvMhPN8RDrNkn3yVTlydxjIl2MXfgnn9YZghD/JLjyB3zkLGT48y63Dh4z1EWcpLriRdbxMsR349n26UN3tOnzF3bWqItPhkD7bcV5ibtyrUScyauZ77EXPKIPHAt5givmVu0jRK5aBtvaTMELvJKLJSyXdiOM/3a1mlcz2dym+cd12L9knvGEvOtzHnYKXPfxaKcu7En5PUIM9rlOUM+Yz4SD2saBhEXQsSNQHDienDPnLu4UY+PKLymLsaPXewTV7Qj9rxfE1fkO+y0nYmLuZZjxVfszdGni3Vc5xxzE3sx59r2ctqS47yu4cn7nPttwhz+yjk+RJiZmzH2LmbkM+c/j7nP65dXW0frAM7gCZ4Dp7HAcjcSzTXa0I/A4o6QdiyWIcHiAwgxQekbk5QJjS8mGv6Ij/bZfmwgTBjs0BZbvC7jp19sZDGmGDP9GBsFWzke3tOv7Bs2ONM3xxXXoj5sRz02aY9fDl7HuGkT12FMLPjGRp6gCPOb3vSmMHl8Dn/Rj8qoO9FwQ320C/6ZBQs+cyVu6jhox1jIWTlebJb2aJ/Hsy3O0iZ9s8+wn9vRJueN99lnjJU2uT7GQ12Mj7HGWol+5Zk2xEG/bWuJdtjLfkt7+X1eK21zJq5nv5lF2GKeESc2aMv4Svbxvq0/fXM9fvM6jWtlu/Af51i7OT9xrTzDqO3pltgZA2MJO7SN19kO9SVrcsBekZlRV5ZYj/hhzgWzbA+fmStM8prO+cFO7FHleuniRn05rvDJPOI6/ogt2sX4iDn2hPBbjjG/J1bGRj/YhO3chte0CV/ltfw+5khwpl/MsdyO1+gJ/mDEOesL1xFm9j1yEoX3+XdlxxyPMWMDfzlfXf6xCSN8R/+SGePhhqGMLeLpc+4lzHkC9TG6ljbEDbx9jX8tHHeNg8nLRnBIhY2GDYTFVxYWKBvpFIWNgbnL+TQWODP+Ns4lj7iJyZtx2cb3EqhFgLnLjUOfudsVk8LcRcb60QQOUZi50eBjt7bCE0S+625r07cOgYffaS2MnRudvmVTXvrasJ0ExhKY4mmZGBTmsZmwfyeBQxRmns64G87fUfE9Ht/fjr1L5pMdvjfje7/T+LQc44/vaNs+Nu6cbC9+L39aP2HYxMVr9Qgw/8Z8hB2RbhVmNtd9nezEvc/xR5L29dz3O6Z9Gx+CwdiYWxw82fHx1ZiPrmCADeyV3z/uG59d443xw3OoKO/q034SWCOBrcK8xqCNSQISkIAEJHCoBBTmQ82s45KABCQggb0koDDvZdoMWgISkIAEDpWAwnyomXVcEpCABCSwlwQU5r1Mm0FLQAISkMChElCYDzWzjksCEpCABPaSgMK8l2kzaAlIQAISOFQCCvOhZtZxSUACEpDAXhJQmPcybQYtAQlIQAKHSkBhPtTMOi4JSEACEthLAgrzXqbNoCUgAQlI4FAJKMyHmlnHJQEJSEACe0lAYd7LtBm0BCQgAQkcKgGF+VAz67gkIAEJSGAvCSjMe5k2g5aABCQggUMloDAfamYdlwQkIAEJ7CUBhXkv02bQEpCABCRwqAQU5kPNrOOSgAQkIIG9JKAw72XaDFoCEpCABA6VgMJ8qJl1XBKQgAQksJcEFOa9TJtBS0ACEpDAoRJQmA81s45LAhKQgAT2koDCvJdpM2gJSEACEjhUAgrzoWbWcUlAAhKQwF4SUJj3Mm0GLQEJSEACh0pAYT7UzDouCUhAAhLYSwIK816mzaAlIAEJSOBQCSjMh5pZxyUBCUhAAntJQGHey7QZtAQkIAEJHCoBhflQM+u4JCABCUhgLwkozHuZNoOWgAQkIIFDJaAwH2pmHZcEJCABCewlAYV5L9Nm0BKQgAQkcKgEFOZDzazjkoAEJCCBvSSgMO9l2gxaAhKQgAQOlYDCfKiZdVwSkIAEJLCXBBTmvUybQUtAAhKQwKESUJgPNbOOSwISkIAE9pKAwryXaTNoCUhAAhI4VAIK86Fm1nFJQAISkMBeElCY9zJtBi0BCUhAAodKQGE+1Mw6LglIQAIS2EsCCvNeps2gJSABCUjgUAkozIeaWcclAQlIQAJ7SUBh3su0GbQEJCABCRwqAYX5UDPruCQgAQlIYC8JKMx7mTaDloAEJCCBQyWgMB9qZh2XBCQgAQnsJQGFeS/TZtASkIAEJHCoBBTmQ82s45KABCQggb0koDDvZdoMWgISkIAEDpWAwnyomXVcEpCABCSwlwQU5r1Mm0FLQAISkMChElCYDzWzjksCEpCABPaSgMK8l2kzaAlIQAISOFQCCvOhZtZxSUACEpDAXhJQmPcybQYtAQlIQAKHSkBhPtTMOi4JSEACEthLAgrzXqbNoCUgAQlI4FAJKMyHmlnHJQEJSEACe0lAYd7LtBm0BCQgAQkcKgGF+VAz67gkIAEJSGAvCSjMe5k2g5aABCQggUMloDCvNLPPPfdcw/Gnf/qni0X40Y9+tHnjG9/YHB0deUzA4Pu+7/uaP/mTP1ksnzo+PQRi/zg9Iz6skSrMK83nhz/84WMxXFKYP/ShDx3H8N73vrdZ6/Ge97znOMbv+Z7vWW2MwY4bHIV5pQvuwMJizn3FV3zFgY3q9AxHYV5prtckzH/2Z3+2UkpN8+Uvf/lYmN/3vvetNkYCe+aZZ47jVJhXnaaDCU5h3u9UKswrzZ/C3C8xCnM/TrY6XQQU5v3Ot8K80vwpzP0SozD342Sr00VAYd7vfCvMK82fwtwvMQpzP062Ol0EFOb9zrfCvNL8Kcz9EqMw9+Nkq9NFQGHe73wrzCvNXwjzO9/5zoafPF7ieNvb3nb8A0v+8Nf4SRI//PWOd7xjkVwuMX/0ucy6hft3f/d3+1PZ45ftYhYU5sXQb3YcwvxVX/VVzVLHV37lVyrMm9PU+2oI81K51O9y62gJ9qxd/7tU7+W5uoYK8+pS8v8HFMK8hv/H7BPz+EkSwux/lxrPUgvbCfhR9nZGa26hMK80Owpzv8T4HXM/TrY6XQQU5v3Ot8K80vwpzP0SozD342Sr00VAYd7vfCvMK82fwtwvMQpzP062Ol0EFOb9zrfCvNL8Kcz9EqMw9+Nkq9NFQGHe73wrzCvNn8LcLzEKcz9OtjpdBBTm/c63wrzS/CHMb3rTmxb9s4/x16W+9mu/tlnzwX9H+eqv/upVxwi/M2fO+NelVrreDi0shJn9w7KfBBTm/cxblaj5b1JPPfWUx0QMbt68WSVvOpGABPabgMK83/kzeglIQAISODACCvOBJdThSEACEpDAfhNQmPc7f0YvAQlIQAIHRkBhPrCEOhwJSEACEthvAgrzfufP6CUgAQlI4MAIKMwHllCHIwEJSEAC+01AYd7v/Bm9BCQgAQkcGAGF+cAS6nAkIAEJSGC/CSjM+50/o5eABCQggQMjoDAfWEIdjgQkIAEJ7DcBhXm/82f0EpCABCRwYAQU5gNLqMORgAQkIIH9JqAw73f+jF4CEpCABA6MgMJ8YAl1OBKQgAQksN8EFOb9zp/RS0ACEpDAgRFQmA8soQ5HAhKQgAT2m4DCvN/5M3oJSEACEjgwAgrzgSXU4UhAAhKQwH4TUJj3O39GLwEJSEACB0ZAYT6whDocCUhAAhLYbwIK837nz+glIAEJSODACCjMB5ZQhyMBCUhAAvtNQGHe7/wZvQQkIAEJHBgBhfnAEupwJCABCUhgvwkozPudP6OXgAQkIIEDI6AwH1hCHY4EJCABCew3AYV5Zfn7pV/6peZ3fud3Fonqp3/6p5s//uM/XsT3O9/5zuYv/uIvqvv+0pe+1OB7ifLCCy80P/VTP7WE6+ZTn/pU86EPfWgR3x/72MeaZ599dhHfv/zLv9z89m//9iK+T+P6WgT0AThVmFeWxNMqzD/+4z++mDC//e1vX2QWKMz1sZ9WYV5qfdXP8GF4VJhXlkeFuW5CeGJWmOsy94m5Lm+8Kcz1mY/xqDCPoTdDX4V5BqgbTCrMG+DMdElhngnsBrMK8wY4K7ykMK8sKT/zMz/T/ORP/mTza7/2a9WPH/iBH2iefvrp6n4Z61vf+tbmwx/+cHXf3Ah97/d+b3W/jPnnf/7nm+///u9fxPd73vOe5od/+IcX8f0TP/ETzY/92I8t4vtHf/RHm3e/+92L+F56fS3xMxwr2173JhyFeWWpQpiPjo48ZOAccA5MNgcuXry4yM9wrGx73ZtwFOaVpepnf/Znmzt37jSf//znqx9PPfVU84lPfKK6X8b6Iz/yI81nP/vZ6r7/8A//sPnBH/zB6n4Z8/PPP3/83d8Suf71X//15n3ve98i4+YHsPiJ8CXGffPmzeb27duL+F56ffnEvLLNfkM4CvMGOEtc8jvmutT9jrkub7z5HXN95n7HXJ/5GI8K8xh6M/RVmGeAusGkwrwBzkyXFOaZwG4wqzBvgLPCSwrzypKiMNdNiMJclzfeFOb6zBXm+szHeFSYx9Cboe+Swsz3fkv95i98L/EdGMK81G/A4heMLOXb3/w1w+LdYvI0rq8tSLzcQUBh7gBjtQQkIAEJSGAJAgrzEtT1KQEJSEACEuggoDB3gLFaAhKQgAQksAQBhXkJ6vqUgAQkIAEJdBBQmDvAWC0BCUhAAhJYgoDCvAR1fUpAAhKQgAQ6CCjMHWCWqH706FFz+fLl5tKlS1Xd37p169gvv6Ob36nLryysUR4+fNg88cQTzfnz549/JzBjr+U7jw/uxFCL+927d1/xO5Br+WbcjPfJJ598ifuVK1cyjllet405fic81+YuDx48aB577LHm7Nmzx+Nm3sGhRgnftdYX42KsrOuyEAvrLGLhvWV9BBTmleSEzYlN4+rVq9UEgqEjjmzMsUCJ48yZMy+9nxMPG8f169ePY8BP908UCgAAFQ5JREFUbN4Ry5y+s23GjzDWEkfGWctXHiev2bS5+WKekfslC/6ZazUE8ty5c8dzLRiQ82vXrs0+fMbGug6RjPXFeY6CXcZ64cKFl8YbfiIWfl84hXMt/hGD534EFOZ+nGZvxcJFkJbctGOQiAaCuUTBd2xiNfzzhB7jrSWW4bPG+EofbMa1xln6Lt9zc1BrnvGEmAtzrAaHNj/UMfY5CmNiH4FrybYt99yghFDPEY82dyNwcrbuZsNeExJYgzBzt11THAMfT1A8XdR6YsYfTxec2cRqbNSMNTZNxonvmoUxLpHbcoyMG7GsNX7GfePGjeMweHLk49xSuMoYp3iPj/LJnDXOpxZzlphj2UfbjRDtEGfLuggozOvKx+JPzGwaNT/eYmNmw+Q7P24Ian7HzGYd/tigagkzwghj/HFjkD/qnHs6IoY8IcEa/8SxhFDDe66nxjaGiDHjRZD5eYJaT4nML/zlwtjLJ/h8fYrX+ODIhfGXdeSeesu6CCjM68rHosLMExwigTjXKghzbCKxcbKJzl3YmPOTAjEstUHBu9bTI36yIJLzWr4jp/FdZ62nZfwiQNwEkXfyzBNrrU9mmGeIMz9wF9/vzz3XYk0Fc874LIV5ya9Vcmy+PklAYT7JY/F3bNJzL9q2QbJZsnks8fSU42ETKz/6y9eneI0gIEZslDytc8STVHzcOYWfITbIeY2nOMZd3njhu2bea98EkW8+GchCDOvySXZIvoa2xTfciSVuDobaGNK+TZhZV6Uw187FkDGc5rYK88qyv4QwI8rcydfcnLuws1HkJ9mudmPq2Rxj44oz4sQTFe+XKLU+xmec8fF9jLOtLq7NcYZzeXMwh5+w2bWmuElZotS4EYp5ncfHDUG5tvj0ZO4b4RyDr/sRWGZm9ovtVLbq2kTmgrGkKOcnGMaHYHKDUOPJseTJRsaGWaMgjIyVAn+e0hErXs9duPmCcfgiBzxNRjw1/DPWmiXGmOcbc6x2HIyZeVbDb5swk3NuRoID59pfXdXM+z77UphXlj0WS807WBYwi7XtmBsNd+v45WNkDgSCeJYoCFYt7iEKjJ1NGg61hBG2jBPWwbx8gp6TP2Ot6S/GAvMYMzcmcA+BijZznRkzrBFBnljjpmguf9htE2bqmedwgAHzbw2fks3JYV9tK8z7mrkDiptPCWoK0wGhGzWUWsI0KsiJOzPPaghjDht/a2Nd86uEzMLX/QgozP042UoCEpCABCRQhYDCXAWzTiQgAQlIQAL9CCjM/TjZSgISkIAEJFCFgMJcBbNOJCABCUhAAv0IKMz9ONlKAhKQgAQkUIWAwlwFs04kIAEJSEAC/QgozP042UoCEpCABCRQhYDCXAWzTiQgAQlIQAL9CCjM/TjZSgISkIAEJFCFgMJcBbNOJCABCUhAAv0IKMz9ONlKAhKQgAQkUIWAwvwiZn7J/Vy/z5bfS8svjK9ZPvjBDzavetWrtrpcIratQc3UgBzk3xHML/CfK+dtQyj9t7WxbncC5Hbb71znL4gt9YdSdh/ZdD1rjp/fEf7EE0+89Ady2GP7ll33pb/5m79pvuZrvqb5yEc+8pIr/qTqO97xjpfez/mC+TfFnlJXLeYkMtI2m2a5YKcATFi7TrIxQ2oT5rZf4L9EbGPGNaZvKYz8lZ1af1GKuEv/Y8ayL33ZnLeJ5RRj4S9Wkc/8Byra1m9NYZpiXFPbqDl+9lP+ilfsOzk328a1676EMLPOlhRm5iHxjykK84v0WMR54rDQmcRTlF0n2RjfbcLMnWM5YZaIbcy4xvQthTE2jDE2h/Qt/Q/pu69tufEpb3inHgvrlj+pmP+EIeu5bf3WFKapxzmFvZrjH+Nr131paWEmR8z38+fPj0rXVmGOjZzJf+fOneNj0x0wC4J29+7de0Vg9Iu7WF4/88wzrxCKshN+sfX0008fn7N40jb7K+OKvrTj2qb4GWf0p99jjz12/DdL8c2R/YbdrnGWY8iTLOLAZvgr2/OePtvs5zY5PvqXwhwxBMfIQ9TTp29sOd7gT6xhM67znriCF/ku20RbzmGr5M01bETfiBOfXQyzz2hTCiP24lr4j/dcI15i6SqwC56044gY2/qE/4itjB/fXf6wu8k2/qJ/xFTGENfbchX923xEv7AX8Ucf7JVjiWtsUHycGXzCBuwo2KJvzI02/7SjnjjaSvyt5biGTXzyN4fDb/TNYkEM+O5ijr0Ye9v44npbzNEvYuIc3GJe8b4s2MJXl7/MjbiDW2kn3tM+t8njjzbEir+IK+o3nfNYusYP/8h9W5tsP2IgDmwTN+ulrcSY2vI2RJixgz/slLngfcRMbLChfZTIbxsz+hJ7vlGMfn3P7SNPvXHAHQB3pCSVjyaoY/HnQjCIGY/x0Y7EMIAo2OEaAWODJ7hNd9O0w2/YpE/+noL+cY3XXM8wIrn04VrEVd5dEx/XIxb+mDl2wzb9Ikk8SdMWf9TThs2nTGyMmXPEEeOmX1u8tMUP3OAc9uGaC20YQ7ThzPvMOgtzPP3nuOMj3IiNscd4iQ179NtU+APw0YdYsR926Ucd7+GDzRgzi7UstKN/sMF/MKdtxJlzGfZyzmlb8iHGJ5988tg+dqLgK3JOHe/jj9oH28hvZkuuY0y0jzg45/GHnzgzPuJnbLSNfjdu3DhuQty0yeOOvsSzyXbbWslcglv2C5M8b2EBg7JQT1xRIhdt8xk/FHhhC37BMtuOccKR68QV9nJM2OI97fN4IhbO9CUPUeCEzTw3oy8xcL1cY7wv/ca4S2bhh3NfZjEnsUUMxMeY8rwKXpxpx/XgGT6pw2fMIdpSV64pxsKYggH+Ll++fGyX/lFijNjJ/uN625l9Af/YDP/sUcEv9puc+21zlzEEG/qRT+pygSHzJcbEmTHm9dJHmGGeY2McbfsNY4s5SWwxx6IuM+N6LmgIx67l5MhbrACHQeQJFIs8b3KxQUVyOBN4FhUmQWxo2V6L22N/+M6TiHZhn9fZP+9pC+AosYEAPvdjkmA7x1D6CuhhK87Yyf14D59yAUV7zhEHySvjyPHSNk8A3uML+yWHcuyMMSYO/bIw857CGMt+ERuTKMfG+zK2F80cn6Jf7sPr/B6Gpc/oFxslxnjNGPMCYyzMlSjRrw9DWORFQUz0K2Mhvsw14s119MVe3liIjba5lHnL1+I1/stxxiJnfJQ2OzH2PO/CJue4nplSH7mI6/lGC9aMK4+V1+W4sEM9sUcJe/QPH23tqCsZhw3sMb9yzNhqW0sxP7KvsEMdtrKdiKVtLNSV7WON5TUMK9ox1igwK+Prywy/eQ5hs8wn9vMYY58N/5yJqZxDEWu5fsr8MG/pHzkPdnletMWV/RMzNjIr7LTN267cb7MXnPGTC+PJexx+y32ijzBjM+eV98Sa94yY44wrc6UtcW1jBuNN+2ceV9vrkyNvaUEQ5YSiGZByfVuwMWHCbCzwcqBxPZ+xjY8hJWBGn3hfLloSyuSOCUp74s/vSRRHn0K7PGHKPhFHmcyojwUa74kvF1hwZ7ipEHuOd6gw4zuXiCXX5ddxveyX23QxhFWOtZxL2GCOkJOwH/76MgymEU/MxbBHPTGUOW+bc/DP8Zb9sFXyD7/53LWW8qbGXC0XNLxo01XYULZdz5tO2CHm7KtrDNQTe5TIRbmuypzRvo0V9dhrWzNtcz3ziRjiHLHkvHKtayzE08aK+hxP+T78Ed8uzOCfnyrD3qZzjC3vl11ziPqcj/I9fthXqIdNfp9FdlM8XGP8bWsk1lded125zz6Ipc0efog1Stgv98a4UYh9oa8wh904l/Ml2IfdaMeZuLYxi/5lvNnOptcvj7yjVVuCacpEAzwlgkA8+LgkDt7Tn+sUBp9hH1d2/IPtts0kN2fQfKTOR4H4DH/RJuIK/1HPuVx4xBUTNq7H+HI/XmMPn3wUiF8WalfbaI/9vMByfcQXfIJfnHlyLLlhK8dAmxzDUGFuG2Pps2wTi4cNh+9qykI8mWlcpy5vbvgp5w5jpz42m8hlXvjYi/qSYfiKcx/RIN7MMPoSb66PTTauc+Z63tjztXjNeNp40C/sM6e5aYxx07d8H/biTN82u3Gdja/terCLzaMcZ/SnPs+F6BfMox1n2uX6rtho17bpxUYbayXeZ5vZX8RSzouusRBP275CPUeUrlyFQES7Lj8lM8ZDHpj37BtlvNjL+xlrKvazPHbiynMj4sjxBpPcL9qVDyTYoi+++L50W+ni15Yn2sJhUym5R9vy0wLstAk47fOY+goz8bJ/hnaU+2cwjHjyGWb4DGaxfnKb6N+Wg9yu63UvYW5bQHkziSDYqAFYHjEJqWcS9CkkDHtdJSYUcWCXGMpkRlxtcMoJlic2PtsmDAkgGUwQYiMGbHMHTvuuEnGU16M+4gs+Jb94H/0ZMxMjx5DzQbsawowfNpyIh8kdGyrXYELsZYlxRj3sI48x1jgHm2AVfeIc9dGOfsTRVvAT7bhexteWc9phM+eXMcKfm4dY2MyJtgWa48B/2512aR8W2KbEPN9kG9/Y6Crl3I52wS5yVsYR7ajHRpTol1nGtW2Mu9pFPWdYw4CC764NmesRS+wxx51achb1Zc5zfc5xV67CX+SjL7PwQz7xg30EOgo5QLS5acAmfmI/y5xLvtGfevpRIsaSCdfaxk87+jKniaFtvw8/9I/cRF2cy9jafEXbOHfZizFEO+Jj720r7L8x9j7CTPyxf8IYX9QRS5TSf9THOZgxN9uYRX/Ou5SXV1tH75zw3CQDJcgyKbltvAYe7foUJmgGVfYJsLk+NrGoCzhtEy0nk/blOPFd+g/7sSjDT1vbuMY54sh1uT6SV96Nl+15zwIm1nJM5cSqJcwRI0xgmhcPXNqeTrihoG0UchkLK+rKc1+GkaOu/sGa68SX/XblkTZcy4VxwTwWdr7W9bqcY9Gu9Bs5Zl2Fn2jbdqZ/G+doy/W2m9xyvjHOtpsa6vO6jVxkluGLdrke35lxV7uo50wO2ewo2246umJpyxn2uuKhniNKl98Qy2jX5QfemVm0j3PEzVgprIcyh9Em8yz5hj3qg/Om/Ti3i775zJwO9rk+XhNj5hT1EWvc5FHfxTr6cC73rbjGWDK/knu040w7rlO2CXOwKfdP8pXHFePJfrpeB7OsC9Eff7uUrSrJoPNmi5PYOGJSUcfEyj/o1RZMCbutTdTFBpsTHdc4E1f2Tx2TJicz4AAul6jnHIV+MbGp43U57rb4I9E5qWEzzuEv3sc56iMOErtNpKJPTjj9WEw5hi5hLidk2IuY4txVH9fbziyO/HRDPMSVJ2zEmoWC/CAIuV1pvyueqOdMiXyUcyNyF+1oS3w557zPDCMG2uT68BnX+56ZY6XwMea2ucx6ipjLnJX+aFdyzm26rpfjjTVX5oGctq2rzDL80S7X4yPnuqtd1Mc51gH28lyP63Hu4seYS9b0IR6ulaVkEf1LFuQl7ye0I8ayXcms9Md72oSgxHhzuxD3zLPkG+2pz+PCdo6TdjFvc7voH+doU44nrsccKfdlfOEzly7WuU3YK3NMX8YUpWtdl/23CXOMLzNlrMwVfEaJdvF+0zn0MI9h043EJltx7eWRR01xBg6TkY/W+D6Xg02AulwIjskVH+/F9595csQkzv02vcYHvuK7AD76IREUIHKN70U48FsmM+ASFzcNtMMW/cq703Jix9ME7fn+FOgxOeI7Va4xGfGbk1qOKeLoqs+TJCYaPmLc/FeIWMBMIsbDTQNxkY+YVDmGNmGGZ+5HPNtiK2OO9/QjRvzz/wCDa96EiQefHFynLf6JPy98XsORccSYOecbo21xZobEQI6ZL+ScMzGQ49yO+PImxfvMMMZKm1zPPGAMxMu8iwNfm0rbWmKMxFYW5gFjKDe7sl28xwbtg19eK/DlOr4iD8TMGPJmEnOLa+SKg9fBLnxFLjLLuFYyjlzAhiP8le2if5yjX+Ye18oz8eV9husRIxyYnxEr9nLOwxb12VdmFizgRz5iDPSNuQAnxhfzFluMMQrxEQtrloO1k/nHePGFHdY8fUpO5fuwT30eV+xf2Im4iJ0j2sGEuIkZRvhmjJlD2M9n4oq5hu0YSzCOttgJX1HXdiZ/rKXgjL1yztEvxA6O+OXMuGNvpM02YaYNDBgnNmLM5ScBMX+64i2ZEW8upb18rc/rl2dOR+uYCAwe0BwZRO7GJCUR0Y4E5rt9NhuuDSn4ikFiL+7UWDhM5vCVBTvsB1xiiPix1RY/dsJG9I8+XAu/2CzjoR+xdBX6to076sN29Od9HlseN224HjFwJqboEzYQ5scffzzeHp/JT+4XtjbFdsJAelPyb+NHHfMB/uGXcdG3LNTlXNM+54PxbYqT67nQt/RZciSW7IP3HGUp80ubGBvj46COjbZtboW9iB97vOagfRePcrMNO13nzK8ca/ANv8Tc5pc5Qt9oR6wl+3hfMicu+uV6fAQvrmG/rV05Jmww/pyfsk28ZywIRVky59iHiKXNJvUcuRA7toNFFzNiDWbMOXxRR78oeQ1QT/tgQZvSV8wj7GWe9M3vwz715bjYFyIuzvTDbrTDJ+/pGwfvqd9WMlu45bFE3y7WcT3OZRwRAzGVpdx/eZ8Lwsy+l38lJ+/z78rOczznK+e/zF/2sY0Z4+k7d7Pd/Lq3MOdO+/KapMWNxb7EfEhxsrDYzA6psCi5424rLOy2zaSt7ba6mLttG962vodwnY2fG50+hY2QtiE4ffrYRgJzEWDP6zt3u2JQmLvIWD+awKEKMx+7lQVxoH6qGxHYcTd/GssuLLlhgj99LRJYigDzj09v4hOaXeNQmHclZ7+tBA5RmFl43A3Hd3N818R3XYgC3zONEQaekrGHbXy0fWS5FfoeN4jx78qS/mP47zE6Q18JAT7hYh6OLVuFmSeAff04jbj3Of6xyV26Px8tTjFJlx5H6Z/Nn7Ext+KYYpzYCHv7uuZKVkPex/jjO8YhfW0rgUMisFWYD2mwjkUCEpCABCSwdgIK89ozZHwSkIAEJHCqCCjMpyrdDlYCEpCABNZOQGFee4aMTwISkIAEThUBhflUpdvBSkACEpDA2gkozGvPkPFJQAISkMCpIqAwn6p0O1gJSEACElg7AYV57RkyPglIQAISOFUEFOZTlW4HKwEJSEACayegMK89Q8YnAQlIQAKnisD/B3phrDpEy5yOAAAAAElFTkSuQmCC[/img][br][br]One value in the set is an [b]outlier[/b]. Which one is it?[br]
What is its approximate value?
By one rule for deciding, a value is an outlier if it is more than 1.5 times the IQR greater than Q3. Show on the box plot whether or not your value meets this definition of outlier.
Here is the data set used to create the histogram and box plot from the warm-up.
[table][tr][td]1.0803[/td][td]1.0875[/td][td]1.4663[/td][td]1.7978[/td][td]1.9702[/td][td]1.9770[/td][td]1.9890[/td][td]2.1011[/td][td]2.1495[/td][td]2.2230[/td][/tr][/table][table][tr][td]2.5443[/td][td]2.7288[/td][td]2.7344[/td][td]2.8223[/td][td]2.8348[/td][td]3.2484[/td][td]3.3912[/td][td]3.5896[/td][td]4.0334[/td][td]4.1925[/td][/tr][tr][td]4.3763[/td][td]4.5193[/td][td]4.6004[/td][td]4.7081[/td][td]4.7528[/td][td]4.8398[/td][td]5.2050[/td][td]5.2273[/td][td]5.3854[/td][td]5.4875[/td][/tr][tr][td]5.5284[/td][td]5.5506[/td][td]6.6475[/td][td]9.8923[/td][td][/td][td][/td][td][/td][td][/td][td][/td][td][br][br][/td][/tr][/table]
Use technology to find the mean, standard deviation, and five-number summary.
[size=150]The maximum value in this data set represents the spending for the United States.[br][/size][br]Should the per capita health spending for the United States be considered an outlier? Explain your reasoning.[br]
[size=150]Although outliers should not be removed without considering their cause, it is important to see how influential outliers can be for various statistics. Remove the value for the United States from the data set.[/size]
Use technology to find the mean, standard deviation, and five-number summary.
How do the mean, standard deviation, median, and interquartile range of the data set with the outlier removed compare to the same summary statistics of the original data set?[br]
The number of property crime (such as theft) reports is collected for 50 colleges in California.
[size=150]Some summary statistics are given:[br]15 17 27 31 33 39 39 45 46 48 49 51 52 59 72 72 75 77[br]77 83 86 88 91 99 103 112 136 139 145 145 175 193 198 213[br]230 256 258 260 288 289 337 344 418 424 442 464 555 593 699 768[/size][br][br][list][size=150][*]mean: 191.1 reports[/*][*]minimum: 15 reports[/*][*]Q1: 52 reports[/*][*]median: 107.5 reports[/*][*]Q3: 260 reports[/*][*]maximum: 768 reports[/*][/size][/list][br]Are any of the values outliers? Explain or show your reasoning.[br]
If there are any outliers, why do you think they might exist? 
Should they be included in an analysis of the data?
The situations described here each have an outlier.
[size=150]For each situation, how would you determine if it is appropriate to keep or remove the outlier when analyzing the data? [br][/size][i]Discuss your reasoning with your partner.[/i][br][br]A number cube has sides labelled 1–6. After rolling 15 times, Tyler records his data:[br]1, 1, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 5, 6, 20
The dot plot represents the distribution of the number of siblings reported by a group of 20 people.[br][img]data:image/png;base64,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[/img]
In a science class, 12 groups of students are synthesizing biodiesel. At the end of the experiment, each group recorded the mass in grams of the biodiesel they synthesized. The masses of biodiesel are[br]0, 1.245, 1.292, 1.375, 1.383, 1.412, 1.435, 1.471, 1.482, 1.501, 1.532
Look back at some of the numerical data you and your classmates collected in the first lesson of this unit.
Are any of the values outliers? Explain or show your reasoning.
If there are any outliers, why do you think they might exist?
Should they be included in an analysis of the data?

IM Alg1.1.14 Practice: Outliers

The number of letters received in the mail over the past week is recorded.
2 3 5 5 5 15
Elena collects 112 specimens of beetle and records their lengths for an ecology research project. When she returns to the laboratory, Elena finds that she incorrectly recorded one of lengths of the beetles as 122 centimeters (about 4 feet).
What should she do with the outlier, 122 centimeters, when she analyzes her data?
Mai took a survey of students in her class to find out how many hours they spend reading each week.
[size=150]Here are some summary statistics for the data that Mai gathered:[br][/size][br][table][tr][td]mean: 8.5 hours[/td][td][/td][td]standard deviation: 5.3 hours[/td][td] [/td][td] [/td][td] [/td][td][/td][td] [/td][td] [/td][td] [/td][/tr][tr][td]median: 7 hours[/td][td]Q1: 5 hours[/td][td]Q3: 11 hours[/td][td] [/td][td] [/td][td] [/td][td] [/td][td][/td][td][/td][td] [/td][/tr][/table][br]Give an example of a number of hours larger than the median which would be an outlier. Explain your reasoning.[br]
Are there any outliers below the median? Explain your reasoning.[br]
The box plot shows the statistics for the weight, in pounds, of some dogs.
[img]data:image/png;base64,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[/img][br]Are there any outliers? Explain how you know. 
The mean exam score for the first group of twenty examinees applying for a security job is 35.3 with a standard deviation of 3.6.
[size=150]The mean exam score for the second group of twenty examinees is 34.1 with a standard deviation of 0.5. Both distributions are close to symmetric in shape.[br][/size][br]Use the mean and standard deviation to compare the scores of the two groups.
The minimum score required to get an in-person interview is 33. Which group do you think has more people get in-person interviews?
A group of pennies made in 2018 are weighed. The mean is approximately 2.5 grams with a standard deviation of 0.02 grams.
Interpret the mean and standard deviation in terms of the context. 
These values represent the expected number of paintings a person will produce over the next 10 days.
[size=150]0, 0, 0, 1, 1, 1, 2, 2, 3, 5[/size][br][br]What are the mean and standard deviation of the data?[br]
The artist is not pleased with these statistics. If the 5 is increased to a larger value, how does this impact the median, mean, and standard deviation? [br]
List the four dot plots in order of variability from least to greatest.

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